For many people, Google is synonymous with online searching due to its impressive 60% share of the Internet search market. What is less well understood is the fact that most of the world's approximately 800 billion gigabytes of data is inaccessible to any Internet search engine because it resides in inaccessible databases, is unstructured and therefore not machine readable, or requires more search effort to find than its perceived value, a problem that will be referred to herein as search friction.
Search friction is made worse in several ways: by the rapid increase in searchable data made possible by engines like Google being applied to unstructured data, by the increase in data accessibility via the web, and by the fundamental increase in amount of data being created and stored. Search friction is minimal when a search process works well and becomes intolerably large when the process entirely fails to achieve desired results. The 2010 Nobel Prize for Economics was awarded to scholars who identified search friction as a major economic problem for employment and other important markets.
Existing data construct models underlying user queries and data store design possess inherent limitations impacting how data is captured, retrieved, analyzed and presented. Current systems can only support two types of information: text (and text strings) and numbers combined using very simple logic: AND, OR, NOT, <, >, =, and mathematical functions. These constrained data constructs lead to a number of significant challenges.
Current information systems lack the ability to express and model targets, preferences, importance, beliefs, and estimates—the fundamental parts of human discourse—when leveraging technology to search, match, filter, forecast, evaluate and decide. Further, with increasing movement toward crowd sourcing and social networks, there is a correspondingly growing lack of ability to express and fuse multiple targets, preferences, importance, beliefs, and estimates.
E-commerce solutions have suffered many limitations over the years. For example, many search tools only allow searches for specifically defined items. Said search tools provide no mechanism for the user to express uncertainty in their query and provide no mechanism to express or capture uncertainty in the stored data. Indeed, search tools that do not recognize these uncertainties provide significantly fewer useful results, if any, to users.
For example, employers typically want to find the best possible job candidates. If they set search requirements too tight, the search result is usually empty. If they set search requirements too loosely, however, then there are often too many resumes to read and too little guidance for closure. On the flip side, job seekers generally want to find the best possible position. If they set their desires to high, they will likely find no positions. If they set their desires too low, however, they generally undervalue themselves.
Current systems tend to limit both employers and prospective employees to expressing information as deterministic, i.e., single-valued values. While some information includes single-valued, demographic facts, e.g., age, years in last job, etc., much important information that expresses both parties' preferences, targets, importance, beliefs, and estimates, are not expressed well, if at all, in this manner. Similar limitations persist in other areas such as social networking applications and websites, for example.
Thus, there remains a need for improved searching and search-related tools and techniques, particularly with regard to matching engines as pertaining to areas such as employment and social networking.
Today's social networking sites often provide users with an overwhelming amount of information via a social stream. Unfortunately, current filters typically fail to perform in accordance with users' desires. For example, some filters only put the most recent entries first and other filters only put the most popular items at the top. Certain filters merely apply simple keyword filtering techniques. These mechanisms, whether performed singly or in combination with each other, do not meet today's user demands and expectations because they lack the ability to enable users to filter and order social streams based on user targets, preferences, and importance.
Email is another area that would greatly benefit from improved filter mechanisms. For example, email providers and applications such as Yahoo and Microsoft Outlook merely provide simple filters such as filtering messages by information in the “From” and/or “To” fields as well as “begins with,” “ends with,” or “contains” with regard to the body or other portions of the message. Google may allow a user to personalize his or her news as received from Google but Google does not order a newsfeed by users' choices. Email applications lack the ability to enable users to filter and order messages based on user targets, preferences, and importance.
Thus, there remains a need for improved filtering tools and techniques, particularly with regard to social networking applications, email applications, and websites.
Traditional situation awareness systems are configured to allow users and companies to manage sales opportunities by creating and linking opportunity records to specific accounts, and track opportunities, including percent chance of close, opportunity value, and all associated data. User-defined fields may be added to each opportunity record and interactions, such as follow-up phone calls and onsite meetings, may be scheduled into an existing calendar. Such systems may maintain a complete history of activities with specific notes about each opportunity. Various items such as appointments, tasks, notes, documents, e-mails, and activities to specific sales opportunities may all be created and linked, and reports on sales funnel and opportunity progress may be generated and disseminated accordingly.
However, true situation awareness requires a complete perception of the environment, something that can only be attained by searching for information in data stores described by preferences, targets, and importance, and capturing estimates and beliefs of subject matter experts, colleagues, and other available information sources. Also, obtaining such information is only the beginning—systems and people must be able to adequately analyze and comprehend the meaning of such information by understanding uncertainty and risk inherent in the results and understanding what additional information is needed and the associated cost/benefit trade-off, for example. Current systems fail to do any of this, let alone project understanding into the future to anticipate what might happen and using the information to support choosing a course of action.
Thus, there remains a need for improved situation awareness tools and techniques.
Thus, there remains a need for improved searching and search-related tools and techniques, particularly with regard to unstructured data.